From Bioconductor release 3.5 on, EnsDb
databases/packages created by the ensembldb package contain
also, for transcripts with a coding regions, mappings between
transcripts and proteins. Thus, in addition to the RNA/DNA-based
features also the following protein related information is
available:
protein_id: the Ensembl protein ID. This is the primary
ID for the proteins defined in Ensembl and each (protein coding) Ensembl
transcript has one protein ID assigned to it.protein_sequence: the amino acid sequence of a
protein.uniprot_id: the Uniprot ID for a protein. Note that not
every Ensembl protein_id has an Uniprot ID, and each
protein_id might be mapped to several
uniprot_id. Also, the same Uniprot ID might be mapped to
different protein_id.uniprot_db: the name of the Uniprot database in which
the feature is annotated. Can be either SPTREMBL or
SWISSPROT.uniprot_mapping_type: the type of the mapping method
that was used to assign the Uniprot ID to the Ensembl protein ID.protein_domain_id: the ID of the protein domain
according to the source/analysis in/by which is was defined.protein_domain_source: the source of the protein domain
information, one of pfscan, scanprosite,
superfamily, pfam, prints, smart,
pirsf or tigrfam.interpro_accession: the Interpro accession ID of the
protein domain (if available).prot_dom_start: the start of the protein domain within
the sequence of the protein.prot_dom_start: the end position of the protein domain
within the sequence of the protein.Thus, for protein coding transcripts, these annotations can be
fetched from the database too, given that protein annotations are
available. Note that only EnsDb databases created through
the Ensembl Perl API contain protein annotation, while databases created
using ensDbFromAH, ensDbFromGff,
ensDbFromGRanges and ensDbFromGtf don’t.
library(ensembldb)
library(EnsDb.Hsapiens.v86)
edb <- EnsDb.Hsapiens.v86
## Evaluate whether we have protein annotation available
hasProteinData(edb)## [1] TRUEIf protein annotation is available, the additional tables and columns
are also listed by the listTables and
listColumns methods.
listTables(edb)## $gene
## [1] "gene_id" "gene_name" "gene_biotype" "gene_seq_start"
## [5] "gene_seq_end" "seq_name" "seq_strand" "seq_coord_system"
## [9] "symbol"
##
## $tx
## [1] "tx_id" "tx_biotype" "tx_seq_start" "tx_seq_end"
## [5] "tx_cds_seq_start" "tx_cds_seq_end" "gene_id" "tx_name"
##
## $tx2exon
## [1] "tx_id" "exon_id" "exon_idx"
##
## $exon
## [1] "exon_id" "exon_seq_start" "exon_seq_end"
##
## $chromosome
## [1] "seq_name" "seq_length" "is_circular"
##
## $protein
## [1] "tx_id" "protein_id" "protein_sequence"
##
## $uniprot
## [1] "protein_id" "uniprot_id" "uniprot_db"
## [4] "uniprot_mapping_type"
##
## $protein_domain
## [1] "protein_id" "protein_domain_id" "protein_domain_source"
## [4] "interpro_accession" "prot_dom_start" "prot_dom_end"
##
## $entrezgene
## [1] "gene_id" "entrezid"
##
## $metadata
## [1] "name" "value"In the following sections we show examples how to 1) fetch protein annotations as additional columns to gene/transcript annotations, 2) fetch protein annotation data and 3) map proteins to the genome.
Protein annotations for (protein coding) transcripts can be retrieved
by simply adding the desired annotation columns to the
columns parameter of the e.g. genes or
transcripts methods.
## Get also protein information for ZBTB16 transcripts
txs <- transcripts(edb, filter = GeneNameFilter("ZBTB16"),
columns = c("protein_id", "uniprot_id", "tx_biotype"))
txs## GRanges object with 11 ranges and 5 metadata columns:
## seqnames ranges strand | protein_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000335953 11 114059593-114250676 + | ENSP00000338157
## ENST00000335953 11 114059593-114250676 + | ENSP00000338157
## ENST00000541602 11 114059725-114189764 + | <NA>
## ENST00000544220 11 114059737-114063646 + | ENSP00000437716
## ENST00000535700 11 114060257-114063744 + | ENSP00000443013
## ENST00000392996 11 114060507-114250652 + | ENSP00000376721
## ENST00000392996 11 114060507-114250652 + | ENSP00000376721
## ENST00000539918 11 114064412-114247344 + | ENSP00000445047
## ENST00000545851 11 114180766-114247296 + | <NA>
## ENST00000535379 11 114237207-114250557 + | <NA>
## ENST00000535509 11 114246790-114250476 + | <NA>
## uniprot_id tx_biotype tx_id
## <character> <character> <character>
## ENST00000335953 Q05516 protein_coding ENST00000335953
## ENST00000335953 A0A024R3C6 protein_coding ENST00000335953
## ENST00000541602 <NA> retained_intron ENST00000541602
## ENST00000544220 F5H6C3 protein_coding ENST00000544220
## ENST00000535700 F5H5Y7 protein_coding ENST00000535700
## ENST00000392996 Q05516 protein_coding ENST00000392996
## ENST00000392996 A0A024R3C6 protein_coding ENST00000392996
## ENST00000539918 H0YGW2 nonsense_mediated_de.. ENST00000539918
## ENST00000545851 <NA> processed_transcript ENST00000545851
## ENST00000535379 <NA> processed_transcript ENST00000535379
## ENST00000535509 <NA> retained_intron ENST00000535509
## gene_name
## <character>
## ENST00000335953 ZBTB16
## ENST00000335953 ZBTB16
## ENST00000541602 ZBTB16
## ENST00000544220 ZBTB16
## ENST00000535700 ZBTB16
## ENST00000392996 ZBTB16
## ENST00000392996 ZBTB16
## ENST00000539918 ZBTB16
## ENST00000545851 ZBTB16
## ENST00000535379 ZBTB16
## ENST00000535509 ZBTB16
## -------
## seqinfo: 1 sequence from GRCh38 genomeThe gene ZBTB16 has protein coding and non-coding transcripts, thus,
we get the protein ID for the coding- and NA for the
non-coding transcripts. Note also that we have a transcript targeted for
nonsense mediated mRNA-decay with a protein ID associated with it, but
no Uniprot ID.
## Subset to transcripts with tx_biotype other than protein_coding.
txs[txs$tx_biotype != "protein_coding", c("uniprot_id", "tx_biotype",
"protein_id")]## GRanges object with 5 ranges and 3 metadata columns:
## seqnames ranges strand | uniprot_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000541602 11 114059725-114189764 + | <NA>
## ENST00000539918 11 114064412-114247344 + | H0YGW2
## ENST00000545851 11 114180766-114247296 + | <NA>
## ENST00000535379 11 114237207-114250557 + | <NA>
## ENST00000535509 11 114246790-114250476 + | <NA>
## tx_biotype protein_id
## <character> <character>
## ENST00000541602 retained_intron <NA>
## ENST00000539918 nonsense_mediated_de.. ENSP00000445047
## ENST00000545851 processed_transcript <NA>
## ENST00000535379 processed_transcript <NA>
## ENST00000535509 retained_intron <NA>
## -------
## seqinfo: 1 sequence from GRCh38 genomeWhile the mapping from a protein coding transcript to a Ensembl
protein ID (column protein_id) is 1:1, the mapping between
protein_id and uniprot_id can be n:m,
i.e. each Ensembl protein ID can be mapped to 1 or more Uniprot IDs and
each Uniprot ID can be mapped to more than one protein_id
(and hence tx_id). This should be kept in mind if querying
transcripts from the database fetching Uniprot related additional
columns or even protein ID features, as in such cases a redundant list
of transcripts is returned.
## List the protein IDs and uniprot IDs for the coding transcripts
mcols(txs[txs$tx_biotype == "protein_coding",
c("tx_id", "protein_id", "uniprot_id")])## DataFrame with 6 rows and 3 columns
## tx_id protein_id uniprot_id
## <character> <character> <character>
## ENST00000335953 ENST00000335953 ENSP00000338157 Q05516
## ENST00000335953 ENST00000335953 ENSP00000338157 A0A024R3C6
## ENST00000544220 ENST00000544220 ENSP00000437716 F5H6C3
## ENST00000535700 ENST00000535700 ENSP00000443013 F5H5Y7
## ENST00000392996 ENST00000392996 ENSP00000376721 Q05516
## ENST00000392996 ENST00000392996 ENSP00000376721 A0A024R3C6Some of the n:m mappings for Uniprot IDs can be resolved by
restricting either to entries from one Uniprot database
(SPTREMBL or SWISSPROT) or to mappings of a certain
type of mapping method. The corresponding filters are the
UniprotDbFilter and the
UniprotMappingTypeFilter (using the uniprot_db
and uniprot_mapping_type columns of the
uniprot database table). In the example below we restrict
the result to Uniprot IDs with the mapping type DIRECT.
## List all uniprot mapping types in the database.
listUniprotMappingTypes(edb)## [1] "DIRECT" "SEQUENCE_MATCH"
## Get all protein_coding transcripts of ZBTB16 along with their protein_id
## and Uniprot IDs, restricting to protein_id to uniprot_id mappings based
## on "DIRECT" mapping methods.
txs <- transcripts(edb, filter = list(GeneNameFilter("ZBTB16"),
UniprotMappingTypeFilter("DIRECT")),
columns = c("protein_id", "uniprot_id", "uniprot_db"))
mcols(txs)## DataFrame with 5 rows and 6 columns
## protein_id uniprot_id uniprot_db tx_id
## <character> <character> <character> <character>
## ENST00000335953 ENSP00000338157 Q05516 SWISSPROT ENST00000335953
## ENST00000544220 ENSP00000437716 F5H6C3 SPTREMBL ENST00000544220
## ENST00000535700 ENSP00000443013 F5H5Y7 SPTREMBL ENST00000535700
## ENST00000392996 ENSP00000376721 Q05516 SWISSPROT ENST00000392996
## ENST00000539918 ENSP00000445047 H0YGW2 SPTREMBL ENST00000539918
## gene_name uniprot_mapping_type
## <character> <character>
## ENST00000335953 ZBTB16 DIRECT
## ENST00000544220 ZBTB16 DIRECT
## ENST00000535700 ZBTB16 DIRECT
## ENST00000392996 ZBTB16 DIRECT
## ENST00000539918 ZBTB16 DIRECTFor this example the use of the UniprotMappingTypeFilter
resolved the multiple mapping of Uniprot IDs to Ensembl protein IDs, but
the Uniprot ID Q05516 is still assigned to the two Ensembl
protein IDs ENSP00000338157 and ENSP00000376721.
All protein annotations can also be added as metadata
columns to the results of the genes,
exons, exonsBy, transcriptsBy,
cdsBy, fiveUTRsByTranscript and
threeUTRsByTranscript methods by specifying the desired
column names with the columns parameter. For non coding
transcripts NA will be reported in the protein annotation
columns.
In addition to retrieve protein annotations from the database, we can also use protein data to filter the results. In the example below we fetch for example all genes from the database that have a certain protein domain in the protein encoded by any of its transcripts.
## Get all genes encoded on chromosome 11 which protein contains
## a certain protein domain.
gns <- genes(edb, filter = ~ prot_dom_id == "PS50097" & seq_name == "11")
length(gns)## [1] 9
sort(gns$gene_name)## [1] "ABTB2" "BTBD18" "KBTBD3" "KBTBD4" "KCTD21" "KLHL35" "ZBTB16" "ZBTB3"
## [9] "ZBTB44"So, in total we got 152 genes with that protein domain. In addition
to the ProtDomIdFilter, also the
ProteinidFilter and the UniprotidFilter can be
used to query the database for entries matching conditions on their
protein ID or Uniprot ID.
AnnotationDbi package to query
protein annotation
The select, keys and mapIds
methods from the AnnotationDbi package can also be used to
query EnsDb objects for protein annotations. Supported
columns and key types are returned by the columns and
keytypes methods.
## Show all columns that are provided by the database
columns(edb)## [1] "ENTREZID" "EXONID" "EXONIDX"
## [4] "EXONSEQEND" "EXONSEQSTART" "GENEBIOTYPE"
## [7] "GENEID" "GENENAME" "GENESEQEND"
## [10] "GENESEQSTART" "INTERPROACCESSION" "ISCIRCULAR"
## [13] "PROTDOMEND" "PROTDOMSTART" "PROTEINDOMAINID"
## [16] "PROTEINDOMAINSOURCE" "PROTEINID" "PROTEINSEQUENCE"
## [19] "SEQCOORDSYSTEM" "SEQLENGTH" "SEQNAME"
## [22] "SEQSTRAND" "SYMBOL" "TXBIOTYPE"
## [25] "TXCDSSEQEND" "TXCDSSEQSTART" "TXID"
## [28] "TXNAME" "TXSEQEND" "TXSEQSTART"
## [31] "UNIPROTDB" "UNIPROTID" "UNIPROTMAPPINGTYPE"
## Show all key types/filters that are supported
keytypes(edb)## [1] "ENTREZID" "EXONID" "GENEBIOTYPE"
## [4] "GENEID" "GENENAME" "PROTDOMID"
## [7] "PROTEINDOMAINID" "PROTEINDOMAINSOURCE" "PROTEINID"
## [10] "SEQNAME" "SEQSTRAND" "SYMBOL"
## [13] "TXBIOTYPE" "TXID" "TXNAME"
## [16] "UNIPROTID"Below we fetch all Uniprot IDs annotated to the gene ZBTB16.
select(edb, keys = "ZBTB16", keytype = "GENENAME",
columns = "UNIPROTID")## GENENAME UNIPROTID
## 1 ZBTB16 Q05516
## 2 ZBTB16 A0A024R3C6
## 3 ZBTB16 <NA>
## 4 ZBTB16 F5H6C3
## 5 ZBTB16 F5H5Y7
## 6 ZBTB16 H0YGW2This returns us all Uniprot IDs of all proteins encoded by the gene’s
transcripts. One of the transcripts from ZBTB16, while having a CDS and
being annotated to a protein, does not have an Uniprot ID assigned (thus
NA is returned by the above call). As we see below, this
transcript is targeted for non sense mediated mRNA decay.
## Call select, this time providing a GeneNameFilter.
select(edb, keys = GeneNameFilter("ZBTB16"),
columns = c("TXBIOTYPE", "UNIPROTID", "PROTEINID"))## TXBIOTYPE UNIPROTID PROTEINID GENENAME
## 1 protein_coding Q05516 ENSP00000338157 ZBTB16
## 2 protein_coding A0A024R3C6 ENSP00000338157 ZBTB16
## 3 retained_intron <NA> <NA> ZBTB16
## 4 protein_coding F5H6C3 ENSP00000437716 ZBTB16
## 5 protein_coding F5H5Y7 ENSP00000443013 ZBTB16
## 6 protein_coding Q05516 ENSP00000376721 ZBTB16
## 7 protein_coding A0A024R3C6 ENSP00000376721 ZBTB16
## 8 nonsense_mediated_decay H0YGW2 ENSP00000445047 ZBTB16
## 9 processed_transcript <NA> <NA> ZBTB16Note also that we passed this time a GeneMameFilter with
the keys parameter.
Proteins can be fetched using the dedicated proteins
method that returns, unlike DNA/RNA-based methods like
genes or transcripts, not a
GRanges object by default, but a DataFrame
object. Alternatively, results can be returned as a
data.frame or as an AAStringSet object from
the Biobase package. Note that this might change in future
releases if a more appropriate object to represent protein annotations
becomes available.
In the code chunk below we fetch all protein annotations for the gene ZBTB16.
## Get all proteins and return them as an AAStringSet
prts <- proteins(edb, filter = GeneNameFilter("ZBTB16"),
return.type = "AAStringSet")
prts## AAStringSet object of length 5:
## width seq names
## [1] 673 MDLTKMGMIQLQNPSHPTGLLCK...GHKPEEIPPDWRIEKTYLYLCYV ENSP00000338157
## [2] 115 MDLTKMGMIQLQNPSHPTGLLCK...QAKAEDLDDLLYAAEILEIEYLE ENSP00000437716
## [3] 148 MDLTKMGMIQLQNPSHPTGLLCK...QASDDNDTEATMADGGAEEEEDR ENSP00000443013
## [4] 673 MDLTKMGMIQLQNPSHPTGLLCK...GHKPEEIPPDWRIEKTYLYLCYV ENSP00000376721
## [5] 55 XGGLLPQGFIQRELFSKLGELAV...GEQCSVCGVELPDNEAVEQHRVF ENSP00000445047Besides the amino acid sequence, the prts contains also
additional annotations that can be accessed with the mcols
method (metadata columns). All additional columns provided with the
parameter columns are also added to the mcols
DataFrame.
mcols(prts)## DataFrame with 5 rows and 3 columns
## tx_id protein_id gene_name
## <character> <character> <character>
## ENSP00000338157 ENST00000335953 ENSP00000338157 ZBTB16
## ENSP00000437716 ENST00000544220 ENSP00000437716 ZBTB16
## ENSP00000443013 ENST00000535700 ENSP00000443013 ZBTB16
## ENSP00000376721 ENST00000392996 ENSP00000376721 ZBTB16
## ENSP00000445047 ENST00000539918 ENSP00000445047 ZBTB16Note that the proteins method will retrieve only
gene/transcript annotations of transcripts encoding a protein. Thus
annotations for the non-coding transcripts of the gene ZBTB16,
that were returned by calls to genes or
transcripts in the previous section are not fetched.
Querying in addition Uniprot identifiers or protein domain data will result at present in a redundant list of proteins as shown in the code block below.
## Get also protein domain annotations in addition to the protein annotations.
pd <- proteins(edb, filter = GeneNameFilter("ZBTB16"),
columns = c("tx_id", listColumns(edb, "protein_domain")),
return.type = "AAStringSet")
pd## AAStringSet object of length 81:
## width seq names
## [1] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000338157
## [2] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000338157
## [3] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000338157
## [4] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000338157
## [5] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000338157
## ... ... ...
## [77] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000376721
## [78] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000376721
## [79] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000376721
## [80] 673 MDLTKMGMIQLQNPSHPTGLLCK...HKPEEIPPDWRIEKTYLYLCYV ENSP00000376721
## [81] 55 XGGLLPQGFIQRELFSKLGELAV...EQCSVCGVELPDNEAVEQHRVF ENSP00000445047The result contains one row/element for each protein domain in each
of the proteins. The number of protein domains per protein and the
mcols are shown below.
##
## ENSP00000338157 ENSP00000376721 ENSP00000437716 ENSP00000443013 ENSP00000445047
## 36 36 4 4 1
## The mcols
mcols(pd)## DataFrame with 81 rows and 8 columns
## tx_id protein_id protein_domain_id
## <character> <character> <character>
## ENSP00000338157 ENST00000335953 ENSP00000338157 PS50157
## ENSP00000338157 ENST00000335953 ENSP00000338157 PS50157
## ENSP00000338157 ENST00000335953 ENSP00000338157 PS50157
## ENSP00000338157 ENST00000335953 ENSP00000338157 PS50157
## ENSP00000338157 ENST00000335953 ENSP00000338157 PS50157
## ... ... ... ...
## ENSP00000376721 ENST00000392996 ENSP00000376721 SM00355
## ENSP00000376721 ENST00000392996 ENSP00000376721 SM00355
## ENSP00000376721 ENST00000392996 ENSP00000376721 SM00355
## ENSP00000376721 ENST00000392996 ENSP00000376721 SM00355
## ENSP00000445047 ENST00000539918 ENSP00000445047 NA
## protein_domain_source interpro_accession prot_dom_start
## <character> <character> <integer>
## ENSP00000338157 pfscan IPR007087 602
## ENSP00000338157 pfscan IPR007087 490
## ENSP00000338157 pfscan IPR007087 630
## ENSP00000338157 pfscan IPR007087 432
## ENSP00000338157 pfscan IPR007087 546
## ... ... ... ...
## ENSP00000376721 smart IPR015880 546
## ENSP00000376721 smart IPR015880 574
## ENSP00000376721 smart IPR015880 602
## ENSP00000376721 smart IPR015880 630
## ENSP00000445047 NA NA NA
## prot_dom_end gene_name
## <integer> <character>
## ENSP00000338157 629 ZBTB16
## ENSP00000338157 517 ZBTB16
## ENSP00000338157 657 ZBTB16
## ENSP00000338157 459 ZBTB16
## ENSP00000338157 573 ZBTB16
## ... ... ...
## ENSP00000376721 568 ZBTB16
## ENSP00000376721 596 ZBTB16
## ENSP00000376721 624 ZBTB16
## ENSP00000376721 652 ZBTB16
## ENSP00000445047 NA ZBTB16As we can see each protein can have several protein domains with the
start and end coordinates within the amino acid sequence being reported
in columns prot_dom_start and prot_dom_end.
Also, not all Ensembl protein IDs, like protein_id
ENSP00000445047 are mapped to an Uniprot ID or have protein
domains.
The coordinate-mapping.Rmd vignette provides a detailed description of all functions that allow to map between genomic, transcript and protein coordinates.
## R Under development (unstable) (2023-02-16 r83857)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.23.2
## [3] AnnotationFilter_1.23.0 GenomicFeatures_1.51.4
## [5] AnnotationDbi_1.61.0 Biobase_2.59.0
## [7] GenomicRanges_1.51.4 GenomeInfoDb_1.35.15
## [9] IRanges_2.33.0 S4Vectors_0.37.3
## [11] BiocGenerics_0.45.0 BiocStyle_2.27.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 dplyr_1.1.0
## [3] blob_1.2.3 filelock_1.0.2
## [5] Biostrings_2.67.0 bitops_1.0-7
## [7] lazyeval_0.2.2 fastmap_1.1.0
## [9] RCurl_1.98-1.10 BiocFileCache_2.7.2
## [11] GenomicAlignments_1.35.0 XML_3.99-0.13
## [13] digest_0.6.31 lifecycle_1.0.3
## [15] ProtGenerics_1.31.0 ellipsis_0.3.2
## [17] KEGGREST_1.39.0 RSQLite_2.3.0
## [19] magrittr_2.0.3 compiler_4.3.0
## [21] rlang_1.0.6 sass_0.4.5
## [23] progress_1.2.2 tools_4.3.0
## [25] utf8_1.2.3 yaml_2.3.7
## [27] rtracklayer_1.59.1 knitr_1.42
## [29] prettyunits_1.1.1 bit_4.0.5
## [31] curl_5.0.0 DelayedArray_0.25.0
## [33] xml2_1.3.3 BiocParallel_1.33.9
## [35] purrr_1.0.1 desc_1.4.2
## [37] grid_4.3.0 fansi_1.0.4
## [39] SummarizedExperiment_1.29.1 biomaRt_2.55.0
## [41] cli_3.6.0 rmarkdown_2.20
## [43] crayon_1.5.2 ragg_1.2.5
## [45] generics_0.1.3 httr_1.4.4
## [47] rjson_0.2.21 DBI_1.1.3
## [49] cachem_1.0.6 stringr_1.5.0
## [51] zlibbioc_1.45.0 assertthat_0.2.1
## [53] parallel_4.3.0 BiocManager_1.30.19
## [55] XVector_0.39.0 restfulr_0.0.15
## [57] matrixStats_0.63.0 vctrs_0.5.2
## [59] Matrix_1.5-3 jsonlite_1.8.4
## [61] bookdown_0.32 hms_1.1.2
## [63] bit64_4.0.5 systemfonts_1.0.4
## [65] jquerylib_0.1.4 glue_1.6.2
## [67] pkgdown_2.0.7.9000 codetools_0.2-19
## [69] stringi_1.7.12 BiocIO_1.9.2
## [71] tibble_3.1.8 pillar_1.8.1
## [73] rappdirs_0.3.3 htmltools_0.5.4
## [75] GenomeInfoDbData_1.2.9 R6_2.5.1
## [77] dbplyr_2.3.0 textshaping_0.3.6
## [79] rprojroot_2.0.3 lattice_0.20-45
## [81] evaluate_0.20 png_0.1-8
## [83] Rsamtools_2.15.1 memoise_2.0.1
## [85] bslib_0.4.2 Rcpp_1.0.10
## [87] xfun_0.37 MatrixGenerics_1.11.0
## [89] fs_1.6.1 pkgconfig_2.0.3